article · Journal of Statistical Sciences and Computational Intelligence
Successive sampling provides an efficient method for estimating changes in population characteristics across time by using data collected in earlier survey rounds. Evaluating a specific mathematical estimator designed for two-occasion successive sampling reveals that it accurately captures shifts over successive periods. The performance of this estimator was tested using both computer simulations and real-world empirical data drawn from undergraduate university records. The findings confirm that the estimator achieves near-unbiased results, keeping bias close to zero. Additionally, as sample sizes grow, both the variance and the mean square error decrease steadily. This confirms the technique's reliability and consistency, establishing it as an effective tool for tracking demographic, academic, or social population dynamics across repeated survey intervals.
Tracking shifts in populations over time is critical for understanding trends in education, public policy, and economics. Traditional sampling can be costly and prone to estimation error. Demonstrating a reliable estimator with low bias and high precision enables statistical agencies and institutions to monitor real-world changes efficiently using existing historical survey data.
This statistical method can be applied by survey organisations, university administrations, and market research firms tracking changes over time. Because the technique has been tested on real undergraduate academic data alongside simulations, it represents applied research that could be integrated directly into analytical software pipelines or survey monitoring systems without substantial redesign.
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Successive sampling efficiently estimates population characteristics over time by leveraging data from previous surveys. While most research focuses on estimating parameters like means, ratio or totals, this study evaluates the estimator proposed by [1] for estimating change in two-occasion successive sampling, building on [13]. Through both simulations and an empirical analysis of undergraduate academic records, it demonstrates that the estimator [1] is nearly unbiased, exhibiting minimal bias around zero. Furthermore, its variance and mean square error consistently decrease with larger sample sizes. These results confirm the estimator’s consistent and efficient measurement of change, making it valuable for monitoring population dynamics over time.
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DOI: 10.64497/jssci.55
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